This paper presents a modified resolved acceleration control scheme based on deep regression of the convolutional neural network. The resolved acceleration control scheme can achieve precise motion control of robot manipulators by regulating the accelerations of the end-effector, and the conventional scheme needs the position and orientation of the end-effector, which are obtained through the direct kinematics of the robot manipulator. This scheme increases the computational loads and might obtain inaccurate position and orientation due to mechanical errors. To overcome the drawbacks, a camera is used to capture the images of the robot manipulator, and then a deep regression of convolutional neural network is imposed into the resolved acceleration control to obtain the position and orientation of the end-effector. The proposed approach aims to enhance the positioning accuracy, to reduce the computational loads, and to facilitate the deep regression in real-time control. In this study, the proposed approach is applied to a 3-DOF planar parallel robot manipulator, and the results are compared with those by the conventional resolved acceleration control and a visual servo-based control. The results show that those objectives are achieved. Furthermore, the robustness of the proposed approach is tested through only the partial image of the end-effector available, and the proposed approach still works functionally and effectively.

本文提出了一种基于卷积神经网络深度回归的改进的解析加速控制方案。解析的加速度控制方案可以通过调节末端执行器的加速度来实现对机器人操纵器的精确运动控制，而传统方案需要末端执行器的位置和方向，这是通过机器人操纵器的直接运动学获得的。此方案会增加计算负荷，并可能由于机械错误而获得不正确的位置和方向。为了克服这些缺点，使用摄像头捕获机器人操纵器的图像，然后将卷积神经网络进行深度回归到已分解的加速度控制中，以获取末端执行器的位置和方向。所提出的方法旨在提高定位精度，减少计算量，并促进实时控制中的深度回归。在这项研究中，将所提出的方法应用于3自由度平面并联机器人操纵器，并将结果与​​常规解析加速度控制和基于视觉伺服的控制的结果进行比较。结果表明达到了这些目标。此外，仅通过可用的末端执行器的部分图像来测试所提出方法的鲁棒性，并且所提出方法仍在功能上有效。并将结果与​​常规解析加速度控制和基于视觉伺服的控制的结果进行比较。结果表明达到了这些目标。此外，仅通过可用的末端执行器的部分图像测试了该方法的鲁棒性，并且该方法仍然在功能上有效。并将结果与​​常规解析加速度控制和基于视觉伺服的控制的结果进行比较。结果表明达到了这些目标。此外，仅通过可用的末端执行器的部分图像测试了该方法的鲁棒性，并且该方法仍然在功能上有效。